Bayesian Marketing Mix Models: The Technical Edge Over Traditional MMM
Discover the technical edge of Bayesian Marketing Mix Models (MMM) over traditional methods and why it's important for modern marketing analytics.
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By Joris van Huët, Founder & CEOUpdated 3 min read
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You pour money into channels that feel right, but you have no evidence of their true impact. You are not failing. The system failed you. Traditional marketing attribution and Marketing Mix Models (MMM) are relics. They are static, simplistic, and cannot capture the complex, dynamic nature of modern marketing.
The Failures of Traditional Marketing Mix Modeling
Traditional Marketing Mix Modeling (MMM) is a statistical analysis technique that uses historical data to estimate the impact of various marketing tactics on sales. Unlike modern causal inference methods, traditional MMM relies on flawed assumptions, such as the idea that the relationship between ad spend and sales is linear, which leads to inaccurate and misleading results. This is why so many brands struggle to get a true read on their marketing ROI.
Traditional MMMs are built on a foundation of flawed assumptions. They assume a linear relationship between ad spend and sales, which is demonstrably false.
These models are also notoriously slow and inflexible. They are typically updated quarterly or annually, providing a rearview mirror look at your marketing performance. This is not good enough.
Let's dig deeper into the specific failures of traditional MMMs:
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The Linearity Assumption: Traditional models often use linear regression, which assumes that the relationship between marketing inputs and sales is linear. This means that if you double your ad spend, you double your sales. Anyone who has ever run a marketing campaign knows this is not true. The reality is that marketing channels exhibit diminishing returns. Traditional models fail to capture this important dynamic, leading to inaccurate and misleading results. For a more detailed exploration of this concept, see our post on the limitations of traditional attribution models.
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Ignoring Channel Interactions: Traditional MMMs treat each marketing channel as an independent silo. They fail to account for the complex interactions between channels, such as the fact that a customer might see an ad on TikTok, search for your brand on Google, and then make a purchase after seeing a retargeting ad on Meta. By ignoring these interactions, traditional models misattribute the value of each channel, leading to suboptimal budget allocation.
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The Data Lag: Traditional MMMs are typically built using historical data, often with a significant time lag. This means that by the time you get the results, they are already out of date. A model that cannot keep up with this pace of change is of limited value.
Frequently Asked Questions
What is the main difference between a traditional and a Bayesian Marketing Mix Model?
A traditional MMM provides a single, deterministic estimate of your marketing ROI. A Bayesian MMM, on the other hand, provides a range of possible outcomes and quantifies the uncertainty around its estimates. This allows you to make more informed decisions with a clearer understanding of the potential risks and rewards.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Attribution ModelAn Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- Marketing AttributionMarketing attribution assigns credit to marketing touchpoints that contribute to a conversion or sale. Causal inference enhances attribution models by identifying true cause-effect relationships.
- Marketing MixThe marketing mix is the set of actions a company uses to promote its brand or product. It traditionally includes product, price, place, and promotion.
- Marketing Mix ModelingMarketing Mix Modeling (MMM) is a statistical analysis that estimates the impact of marketing and advertising campaigns on sales. It quantifies each channel's contribution to sales.
- Marketing ROIMarketing ROI (Return on Investment) measures the return from marketing spend. It evaluates the effectiveness of marketing campaigns.
- RetargetingRetargeting is online advertising that targets users who have previously interacted with your website or content. Attribution analysis shows the causal role of retargeting in driving conversions and improving ad spend.